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Setup SuperConfig

Source: R/190_SuperConfig.R
setup_SuperConfig.Rd

Setup SuperConfig object. SuperConfig is a portable, data-agnostic recipe: dat_training_path is optional, so the same config can be validated, shared, or described without data and have a path bound later (e.g. by the rtemis CLI) before train.

Usage

setup_SuperConfig(
  dat_training_path = NULL,
  dat_validation_path = NULL,
  dat_test_path = NULL,
  weights = NULL,
  positive_class = NULL,
  preprocessor_config = NULL,
  decomposition_config = NULL,
  hyperparameters = NULL,
  tuner_config = NULL,
  outer_resampling_config = NULL,
  execution_config = setup_ExecutionConfig(),
  question = NULL,
  outdir = "results/",
  verbosity = 1L
)

Arguments

dat_training_path

Character or NULL: Path to training data file. NULL leaves the recipe unbound; set it (or supply data) before train.

dat_validation_path

Optional Character: Path to validation data file.

dat_test_path

Optional Character: Path to test data file.

weights

Optional Character: Column name in training data to use as observation weights. If NULL, no weights are used.

positive_class

Character or NULL: For binary classification, the outcome level to treat as positive. NULL keeps the existing factor level order.

preprocessor_config

PreprocessorConfig object: Configuration for data preprocessing.

decomposition_config

DecompositionConfig object: Configuration for data decomposition.

hyperparameters

Hyperparameters object: Configuration for model hyperparameters.

tuner_config

TunerConfig object: Configuration for hyperparameter tuning.

outer_resampling_config

ResamplerConfig object: Configuration for outer res resampling during model training.

execution_config

ExecutionConfig object: Configuration for execution settings. Setup with setup_ExecutionConfig.

question

Optional Character: Question to answer with the supervised learning analysis.

outdir

Character: Output directory for results.

verbosity

Integer [0, Inf): Verbosity level.

Value

SuperConfig object.

Author

EDG

Examples

sc <- setup_SuperConfig(
  dat_training_path = "train.csv",
  preprocessor_config = setup_Preprocessor(remove_duplicates = TRUE),
  hyperparameters = setup_LightRF(),
  tuner_config = setup_GridSearch(),
  outer_resampling_config = setup_Resampler(),
  execution_config = setup_ExecutionConfig(),
  question = "Can we tell iris species apart given their measurements?",
  outdir = "models/"
)